 Karen, welcome to DataVersity Talks, a podcast where we discuss with industry leaders and experts how they have built their careers around data. I'm your host, Shannon Kemp, and today we're talking to Info Advisor Senior Project Manager Karen Lopez about her career in data. And welcome, my name is Shannon Kemp, and I'm the Chief Digital Officer here at DataVersity, and this is my career in data, a DataVersity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To help up and to keep up to date in the latest in data management education, go to DataVersity.net forward slash subscribe. And today we are joined by Karen Lopez, a Senior Project Manager and architect at Info Advisors, lifetime advisor to the demo International Board of Directors. And normally this is where a podcast host would read a short bio of the guests, but in this podcast, your bio is what we're here to talk about. Yeah. Karen, hello and welcome. Thank you. Thank you for having me, Shannon. I love talking with you. So this is going to be fun. We've got four hours, right? Absolutely. We got all the time. I do too. And again, just to reiterate, I just love your, how active you are in the community I do love talking to you. And I love your story. Again, when we were talking about this podcast and who we would invite on again, you were one of the first people that came to mind because I think you have such an interesting story. You've done so much in the world of data. And I think it's important that people, more people hear it. Yeah. OK. Let's go. So Karen, tell me, so what's your current job title and what is it that you do? That's a tough one. So you said senior project manager and data architect. I mean, in my heart, what my mother raised me to be is I do believe I'm a data management professional. She doesn't know that she raised me to be that way. I was talking to her I think yesterday. I can't remember times like such a weird concept these days. But I had requested from her. There's a photo of me when I was maybe two years old, lying on the sofa, reading the telephone book. Now, I couldn't quite read, but I was fascinated always with like data in some sort of structured form, which if you think about the mid 60s, there weren't computers in homes. There weren't a lot of places to get structured data. But it was just fascinated, you know, finding patterns in data, looking at data in columns. It's very weird, but that's sort of like, I know you want to ask me how I got my start and I think that's how I got my start. I love it. So and, you know, so you're a consultant for InfoAdvisors. So you you advise companies on on data practices. Yeah, right. Yeah, and a lot of my work recently has been through word of mouth. I work on a lot of troubled projects and by troubled projects, I don't just mean ones that are late or over budget because that's pretty much all of them. I'm talking about projects where they can't get the software completed and get it out of test and they're facing a million dollar a month fine for not getting it into production. So like super troubled projects and a lot of the things I find is that it's because they didn't understand the data. So it's not that they're not passing technical tests. I mean, there are those. It's more of they're not needing either regulatory or contract requirements for being able to execute a process all the way through that gets the data out in the way that was expected. And that is both fascinating to work on because some of my original education, I for some reason decided to take classes in methodologies and like adult instruction, which I can't even remember why I did this, but that kind of prepared me for doing training and for thinking about how projects are different. And I also came through this thing in the 80s and 90s about buying a formal methodology and then deploying it but you can't just follow a methodology that's universal. You have to tailor it. So I'm always thinking about, I do say the best data architect is a lazy data architect. So let's not do tasks that aren't adding value either now or later. And that's sort of been my why my informal title is Data Evangelist. I just want to be here to be the voice of data usually during a development or a software package tailoring process. Yeah, I have heard that so many times. It's love your data. Yeah, love your data. That's my thing. And my my new sort of phrase is I want everyone to be on team data. Oh, I love it. Yeah. Years ago, we used to talk about where you are a process person or a data model person. And and my answer to that is you have to be both. Like you can't be good at data without thinking about the processes behind it and vice versa. Which would make a good panel, by the way, that one question. But I want everyone to at least be part of team data to realize that we build information systems because there's data. And that's been sort of why I think of myself as a data evangelist, too. So in you beyond being a consultant with and having your own business there, you also have many other projects that you are active in that also tangent to data a little bit. And the, you know, you are you do a lot of work with NASA. Yeah, yeah. So volunteer. My title there was NASA data, not which was a program that started was started there by the CIO of NASA. NASA had been running for a couple of years. This what's now the world's largest global hackathon. Which has participants from over 100 countries. And so it's called the Space Apps Challenge. So if you go to spaceappschallenge.org, you can find all the information about the hackathon that's coming up this fall, actually just in a little bit with that Space Apps Challenge. They they had run it for a couple of years and realized they had so few female participants in the challenges. And the CIO at NASA at the time was a woman and one of their media PR communications person was a woman. And so they created this program called NASA data knots to encourage women and underrepresented people to be a kind of learn to code. But they they knew there were plenty of really great learn to code things out there. So they focus their program on how to work with NASA open data and other open data. And I'm like, oh, my gosh, this brings these two things together. My fan like enthusiasm for space exploration and my love of data together. And so I was part of the first formal class at a beta class before and the first formal class of NASA data knots. And that went on for a few years where twice a year, they'd bring on 50 people interested in learning about data, how to work with data. And it was really cool. We had classes and workshops on anything for how to what Python is and how to use it and also orbital mechanics and how to interpret data, space related data and why it was different, say, than normal enterprise data. So it was really cool. So now we fast forward last year that for the last two years and for this year, it's all still going to be virtual. But last year was the 10th anniversary of that. So they involved 10 space agencies around the world, and that's going to keep going on. So you get to not only participate in a challenge, but you get exposure to all the open data sets from space agencies from other government. Most open data comes from governmental or academic. And for me, that's just been so exciting to do all that. And in the space apps challenge, I'm a mentor and I assist in the judging. And that's been so rewarding to see how people meet the challenges, how they use data and the challenges are very socially oriented. Like, how do we use this data to help people with asthma learn when there's like a bad weather day for people with breathing problems? What data can we use and what can we do with it? So it's all really great. And if you're not a coder or a data person, there are art related challenges where you have to use open data to be part of your art project. And I love those. I want to do that. Like, if I ever stop being a judge, I am entering under that category, for sure. Even though I don't consider myself an artist. Yeah, that's very cool. So many different aspects of data. And you do one more. You have another big thing that you are a part of. I know you do a lot of advocacy for young women and getting into STEM. Yeah, yeah. So I was a former national spokesperson for women in technology for a few years. My focus has been at ensuring that girls, it was so hard to put girls back into my vocabulary because I was trying not to use it with adults, but girls to take more math, science, engineering, like classes while they're like around nine age and 10, because we know that girls actually outperform boys in STEM courses until about 12 or 13 when either their brains go wonky or cultural cultural pressures tell them that they shouldn't be better than boys in those subjects. So I've always worked towards ensuring that girls at that age still maintain a desire and also have learning environments that encourage them because girls and women, we look at challenges and problems generally differently than boys do. And that was certainly a thing in my background. I still don't enjoy doing math just for the sake of doing math. But I love doing math and science when I have a goal in the end. So, for instance, you know, maybe managing investments or something like that. But give me a sheet of math problems and I cannot be bothered. I just cannot be bothered to do math for the sake of math. I know women and people who do that. I don't get it, but that's I want applied math. I want applied scientists. And that's even my underlying degree was in applied technology and computer science, and I loved it. So let's talk about that. So, you know, you got it. So you just talked about your degree a little bit. You talked about how you read the phone book when you were a kid, which I so appreciate because, yeah, no, it's not weird to me at all. So, you know, but so what would you want to be when you grew up? I mean, did you go, I want to be a consultant in data management? Like that was that? No. So first of all, the struggle I had growing up is I didn't even know anyone who worked in computing, right? I didn't know anyone, never been exposed to anyone in high school. I had a friend whose brother had a computer that I used to sometimes get to play on and write programs on, but I didn't understand what the jobs in computing were at all, no clue. So, but my mom did a really good thing. She sent me to a summer school at the local university to take a programming class, like the summer between my, I think my junior and senior years of high school. And I was hooked at that, but still didn't have a chance to do it. My high school had no computers, none. They didn't even have a fax machine. So I took a programming class my senior year of high school that was there was one programmable computer shared by the whole class. And I was able to take that. But I think what I wanted to be was a teacher because my mom was a teacher. So a lot of kids want to be a teacher. And then in high school, I took some horticulture classes and I wanted to study horticulture. And then I found out that most people who get an undergraduate degree in horticulture go on to become greenskeepers or work for like a agricultural science type company like seeds or chemistry or something like that, and neither one of those had a lot of great jobs at the time for women who had never worked on a farm and who weren't really into golf. So there was all of that. So I then decided because I was also desired, desired to be wealthy that I would study computers. And even if I hated that degree, I could now have computer skills so that that would open doors for all things. And it's funny, my mom's version of that, my parents made me take typing classes in high school because even if I was not successful, I could type and you could always get a job if you were a typist. And I'm glad they did because now I'm I'm still terrible at typing, but I'm a touch typist on my computer keywords. And that has benefited me a lot. I'm not a hunt and pecker. So I could just and so then when I was looking for degrees, I decided to take I had a choice at Purdue where I went between computer science. It's a really good program there, information, computer information technology, which was applied computer science or a business degree that had a technology edge to it. So the business degree only had two computer courses. So I knew that wasn't for me. And I knew a friend who was in the computer information technology one. So that's the one I went with and I loved it. I absolutely loved it. And so we're talking now, I'll just say early 80s, really early 80s. That was when relational databases were coming out. That's when things like system analysis and design and process modeling was coming out. So I got exposed to those technologies when most of most enterprises still weren't using relational databases. Ted Codd was writing his papers about how the relational databases weren't really relational. It was a really exciting time to be in the database business. So I was super lucky there that the program I had no idea had things that also interested me, structured data. And so I'd say it's a lot of luck that went into. And I also think it's kind of rare to run into someone of my age who had an undergraduate degree in data that involved databases and modeling. So, yeah, I mean, so how did you use that? And how did you get from there to where you currently are now? Like, how did you start applying that in your jobs? Well, my first job out of university was to design databases and to do some light programming. I was, you know, I had some job offers. I decided that I would take the ones that paid really well. And most of those were in Washington, D.C. And also involved working for the federal government. So a lot of people that came out of my program ended up working for either directly for the federal government or for federal government contractors. So I joined a company, American Management Systems, which coincidentally Bill Inman worked there at the time. And I wanted to be on Bill's projects because he was working with data and that just didn't work out it within the organization of the company. But I just went right from school to doing what I love. And that was database design, data strategies, data architectures. And then after that, I moved to Southern California and I worked for a smaller consulting firm that specialized in information engineering methodologies, which had a lot of modeling and a lot of data. And then I worked for them for a long time. They went out of business and I stayed with my current client, which was here in Toronto now and I'm immigrated to Canada. And because they'd gone out of business, I decided to something I never wanted to do, that I would start my own company and just work for myself, not for someone else. Again, just lucky I made that decision. Totally wasn't part of my strategy to be self-employed or own a business. And that's how I'm here. Well, I love it. That's fascinating, too. I don't think I knew that you kind of fell into it and that wasn't part of the plan to owning your own business. Yeah, yeah, for sure. Yeah. Wouldn't have it any other way now, so it's really crazy. Visit dataversity.net and expand your knowledge with thousands of articles and blogs written by industry experts plus free, live and on demand webinars covering the complete data management spectrum. While you're there, subscribe to the weekly newsletter so you'll never miss a beat. That's amazing. So, Karen, so as a practitioner and probably and somebody who's worked with data for a while and I've probably, you know, the most straight path I've heard, you know, in terms of like you've been using data since, you know, you were a kid. What's your definition of data and how do you work with it? Oh, my gosh, I don't want to give a definition. The loaded question. So one of the things I do a lot is when people come to me with data, database, data management questions, one of the first things I ask them is what do you mean by those things? Because I don't want to force my definition on people that are looking for my help. I might say, OK, that one term, I don't think it means what you think it means. But for the most part, I'm kind of one of those. You tell me what language we're using in this conversation and I'll move forward. And if I think you're using language that are leading to pain when you work with outside vendors or when you talk to your consultants or whatever, or that are misleading to the business, I'll make recommendations that we start using, you know, good terminology and good standard things. But I'm not one of those. Like, I don't see it as a religion or a dogma that everyone has to use the word third normal form correctly when we're working on a project. Like, if we were teaching a class or having a debate on the stage at EDW. Sure, let's do that. Let's agree to common terminology. But I'm there to fix problems, to solve challenges. And as Larry English used to say a lot, I loved it was ease their pain that their decisions over vocabulary, their practices are causing them and their clients or customers. So I don't really want to give one. I don't want to say, you know, data is a fact and information is data that's been processed, like there's all those definitions out there. But my non answer is I don't think it matters as much as most people do. Well, I don't think that's a non answer. I think I really appreciate that approach, because the definition you're saying the definition isn't as important as everybody agreeing. What we're saying to what the definition is. So and I think a lot of that comes from moving from one country to another where, you know, the difference between Canada and US isn't that great when it comes to language, that, you know, one of my lessons learned. I love this. When I first started working in Canada as I came into work one day and I said, I was so pissed this morning, like, and I was about to talk about my car was blocked in and of course in Canada, that met falling down drunk. And I didn't know. I mean, I'd heard that kind of reference. Yeah. But I literally announced to a table full of people in a conference room that I showed up to work completely drunk. And so, you know, I've kind of like, you notice I've said process and program and project like I didn't grow up speaking that way. I haven't adopted all the Canadians speaking, but because I was doing a lot of classes and methodologies and process modeling, I decided to adopt what everyone around me was saying for and I have to think about a process and project and all of those things. So I just feel like there are just some people out there that are just there are celebrities in our community that are adamant that things have to be worded the same way. And one of the examples that's used in data modeling a lot, like there's two ways of thinking about models. They all involve conceptual, logical and physical. But there are two paths based on really good reasons that those that logical and physical mean something completely different, completely different. And people will argue we should only be using the right way, which, of course, by coincidence is their way. And and I'm just like, I'll adapt. I'll just adapt. I might flood that up, but I'm not going to be one of those people who insist that, you know, a consultant comes in and this happens a lot and say all of our entity names have to be plural. Like that's the way to do it. We have to change all of our databases, all of our programs. I'm like, no. There are much bigger problems to fix. You will you will live with the fact that the table is called customer and not customers, because there is no value add into changing all of our table names. Yeah, that makes total sense. I think a lot of companies spend a lot of time just changing names. Yeah, and it's not sure. So then do you see, given this experience and how much you work with, you know, especially with a lot of other companies, you know, do you see the importance of data management and the number of jobs working with data increasing or decreasing over the next 10 years and why? Well, my gosh, maybe I'll be one of those people that gets quoted 10 years from now about a crazy thing they said. You know, the whole concept of leveraging data, collecting data, I just cannot see that's ever going to go away. Like we all know that people talk a lot about the volume of data increasing just by like orders and orders of magnitude. So I get that. But, you know, data is just going to keep bringing us insights. And we already have this long history. I mean, even going back more than 100 years ago that in a competitive world, the people with the better data and who are leveraging it the best are the organizations that are going to continue to succeed. I mean, and that includes whether you're a nonprofit, a not for profit, a for profit, a giant company or a small company. So I got to assume then that there will be requirements for people to be skilled in data. But one of the things that I think has changed a lot, well, it hasn't the desire, the need for it hasn't changed, but the recognition of the need is that non data management professionals need a lot of data literacy, education, practice and coaching. And I'm not just, you know, promoting my own services here. Like what we've learned through the pandemic about what does it mean? If one of the states, one of the counties in a state hasn't updated their COVID data for two weeks, like people are getting all upset. You know, I agree, that's wrong. We've got to collect that data. But data goes missing all the time at the scale we were talking about. Interfaces fail, people for, you know, send it in the wrong format. We data professionals know that some anomaly in a piece of data can be caused by mistake. It could be on purpose. It could be because the person who submits the data was on vacation for a week. So it didn't get submitted for a week. And now all of a sudden they get back and actually seven days get reported at one as one day. We know this, but the general public, even some data journalists don't understand that it's maybe it's they think it's not acceptable. But it really is normal in the sense of we should expect it. We should plan for it. We should have processes and interpretations that deal with that. Ready to mingle with your fellow data governance practitioners. Join us in Washington, D.C. this December for the data governance and information quality conference. Five days packed full of new knowledge, new friends and new strategies are yours when you register at djiq 2022 east dot diversity dot net. Take advantage of our super early bird pricing when you register before October 7th. And then what advice would you give to people who are looking to get into a career in data management? Any aspect of data management? Like, what are the skills they need? You know, what's what's, you know, what is the best advice you can give? Well, first, they should start reading phone books when they're two years old. But in case they didn't do that, I think that gosh, there's a lot of traditional answers here. So the first one I'm going to say is if I were starting again, I'd be going into data analytics and data science. But that's not probably what you really mean for data management because data management people generally, you know, collect, store, curate, protect the data that goes into the data science process. Like, I'd love to include them under our umbrella because they're also leveraging data. But I think if someone really wanted to do a lot of the things that we've talked about of the precursor to data science is I would tell them of course, a lot of STEM classes are going to be helpful. There are plenty of jobs where you never need to be a coder or programmer to get into data management. In fact, I have found that, you know, I would say, you know, it's really close to half or more of the people I know in the data management community have no background. So a lot of people think that, you know, you followed this traditional path. You became a programmer. You decided you liked data. So you became a DBA. You like to be in a DBA, but you hated be on call 24 by seven. So you kind of moved over to be a data architect to design database or developer, a DBA developer. But I think a lot of us, especially like in the DEMA, the diversity community, a lot of people came from business side where they made the transition. Maybe they were, you know, working in finance or accounting and they got interested. They were the data person in the finance accounting. So then they became maybe a business analyst or a subject matter expert on the IT side. And then they decided they wanted to do this under the IT side. Or maybe the data architects are still on the business side. There are a lot of people. So in the DBA world, we talk a lot about I'm one of these accidental DBAs. So there are people who aren't really DBAs, but do a lot of DBA like stuff. And I think a lot of people that came into data management are accidental data management. In fact, when I listen to people talk about how they got into a data career, they almost always use this term. I fell into it. I didn't start out doing this, but I fell into it as if data management is this wide gaping hole in the earth that swallowed them up and wouldn't let them go. So I think definitely. OK, so one of the subjects I did terrible in that I wish I'd done better was statistics, but I didn't know at the time, you know, just how much I should have worked harder at stats going into a data career, even though I'm not a data scientist. I'm not even a data warehousing person. But I wish I understood more about that so I could interpret data better, because we often don't get all the facts. We just get, you know, aggregation, summaries, trends, all of that stuff. And I would encourage people that if you're considering like a college diploma or a trade school or even another four year degree, if you're going to pick a subject, you do not have to pick computer science to get into most IT jobs. I work with a lot of people that are great computer scientists and they don't do computer science once they leave school, the vast majority of them. So, you know, and then the final thing is like all my learning has changed over the last 10 years. I learned through webinars and workshops and online training, like what you guys provide, what some other things. There's just so many opportunities now where you can learn from industry experts where you get that real world feel to it. So, yeah, I can sit you through a class and teach you about first, second, third, fourth, fifth, voice, cod normal form, and I will tell you then that you will never do this in real life. You'll just think it, feel it and do it on your own. And I think even Graham Simpson used to talk about that, too, when he authored Data Modeling Essentials and when he wrote all of his other books in data management, that you need to understand these concepts, but you don't apply them the way they're taught to you, just like an engineer might learn good techniques for in the lab environment, but they're not going to do those. They're just going to know not to mix water into acid or whatever it is you're supposed to not do that I can't remember. Well, I love it, Karen, thank you so much for sitting down with me today. And that's great advice and things. Again, just for telling the story, and I really appreciate it. And for all the listeners out there, if you'd like to keep up to date on the latest podcasts and the latest in data management education, you may go to dataversity.net forward slash subscribe until next time. 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